"folder_path":
dir_path,
"folder_name":
osp.basename(dir_path),
"file_name":
['_'.join(osp.basename(p).split('_')[:-1]) for p in image_path],
"image_paths":
image_path
})
for info in tqdm(data_infos):
for i, image_path in enumerate(info['image_paths']):
image = load_image(image_path, transform=transform)
image_tensor = image.to(device)
features = caption_cnn(image_tensor, subset='test')
sampled_ids = caption_rnn.sample(features)
sampled_ids = sampled_ids[0].cpu().numpy(
) # (1, max_seq_length) -> (max_seq_length)
# Convert word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = dataset.ixtoword[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
print(sentence)
text = sentence.replace('<start>', '').replace('<end>', '').strip()
if not osp.exists(osp.join(OUTPUT_DIR, info['folder_name'])):
os.mkdir(osp.join(OUTPUT_DIR, info['folder_name']))
cfg.CAP.num_layers
).cuda()
encoder.load_state_dict(torch.load(encoder_path))
decoder.load_state_dict(torch.load(decoder_path))
encoder.eval()
decoder.eval()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
for i, data in enumerate(dataloader):
imgs, captions, cap_lens, class_ids, keys = prepare_data(data)
#targets = pack_padded_sequence(captions.unsqueeze(0), cap_lens, batch_first=True)[0]
with torch.no_grad():
features = encoder(imgs[-1])
sampled_ids = decoder.sample(features)
sampled_ids = sampled_ids[0].cpu().numpy()
sampled_captions = []
for word_id in sampled_ids:
word = dataset.ixtoword[word_id]
sampled_captions.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_captions)
original_img = unnorm(imgs[-1].squeeze()).cpu().numpy().transpose((1, 2, 0))
fig = plt.figure()
plt.imshow(original_img)
plt.title(sentence)
#ax1 = fig.add_axes((0.1,0.4,0.8,0.5))
#ax1.set_title(sentence)
directory, filename = keys[0].split('/')